class_docstr: This class is used for GPLVM models to recover a variational approximation of
  the latent variable :math:`\mathbf X`. The variational approximation will be
  an isotropic Gaussian distribution.
  
  :param int n: Size of the latent space.
  :param int data_dim: Dimensionality of the :math:`\mathbf Y` values.
  :param int latent_dim: Dimensionality of latent space.
  :param torch.Tensor X_init: initialization for the point estimate of :math:`\mathbf X`
  :param ~gpytorch.priors.Prior prior_x: prior for :math:`\mathbf X`
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code: def forward(self):
      from ...mlls import KLGaussianAddedLossTerm
  
      # Variational distribution over the latent variable q(x)
      q_x = torch.distributions.Normal(
          self.q_mu, torch.nn.functional.softplus(self.q_log_sigma)
      )
      x_kl = KLGaussianAddedLossTerm(
          q_x, self.prior_x, self.n, self.data_dim
      )
      self.update_added_loss_term("x_kl", x_kl)  # Update the KL term
      return q_x.rsample()
  
file_path: gpytorch/gpytorch/models/gplvm/latent_variable.py
imports: import torch
  from ...module import Module
label_desc: Use image node_id 2 for calling the VariationalLatentVariable obj's underlying member method code with example usage: obj.forward() and returns: q_x